import plotstyle_plt
import numpy as np
import matplotlib.pyplot as plt
from itertools import product
from thunderhopper.filetools import search_files
from thunderhopper.modeltools import load_data
from thunderhopper.filtertools import find_kern_specs
from color_functions import load_colors, shade_colors
from plot_functions import hide_axis, ylimits, xlabel, ylabel, super_ylabel,\
                           plot_line, plot_barcode, strip_zeros, time_bar,\
                           letter_subplot, letter_subplots
from IPython import embed

def add_snip_axes(fig, grid_kwargs):
    grid = fig.add_gridspec(**grid_kwargs)
    axes = np.zeros((grid.nrows, grid.ncols), dtype=object)
    for i, j in product(range(grid.nrows), range(grid.ncols)):
        axes[i, j] = fig.add_subplot(grid[i, j])
    [hide_axis(ax, 'left') for ax in axes.flatten()]
    [hide_axis(ax, 'bottom') for ax in axes.flatten()]
    return axes

def plot_snippets(axes, time, snippets, ymin=None, ymax=None,
                  thresh=None, fill_kwargs={}, **kwargs):
    ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
    for ax, snippet in zip(axes, snippets.T):
        plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs)
        if thresh is not None:
            ax.fill_between(time, thresh, snippet, where=(snippet > thresh), **fill_kwargs)
    return None

def plot_bi_snippets(axes, time, binary, **kwargs):
    for ax, binary in zip(axes, binary.T):
        plot_barcode(ax, time, binary[:, None], **kwargs)
    return None

def side_distributions(axes, snippets, inset_bounds, thresh, nbins=50,
                       ymin=None, ymax=None, fill_kwargs={}, **kwargs):
    limits = np.array([snippets.min(), snippets.max()]) * 1.05
    edges = np.linspace(*limits, nbins + 1)
    centers = edges[:-1] + (edges[1] - edges[0]) / 2
    for ax, snippet in zip(axes, snippets.T):
        pdf, _ = np.histogram(snippet, edges, density=True)
        inset = ax.inset_axes(inset_bounds)
        inset.plot(pdf, centers, **kwargs)
        inset.fill_betweenx(centers, pdf.min(), pdf, where=(centers > thresh),
                            **fill_kwargs)
        ylimits(centers, inset, minval=ymin, maxval=ymax, pad=0)
        inset.set_xlim(0, pdf.max())
        inset.axis('off')
    return None


# GENERAL SETTINGS:
with_noise = True
target = 'Omocestus_rufipes'
search_kwargs = dict(
    incl=['subset', 'noise'] if with_noise else 'subset',
    excl=None if with_noise else 'noise',
    dir='../data/inv/thresh_lp/'
)
data_paths = search_files(target, **search_kwargs)
stages = ['conv', 'bi', 'feat']
load_kwargs = dict(
    files=stages,
    keywords=['scales', 'snip', 'measure', 'thresh']
)
save_path = '../figures/fig_invariance_thresh_lp_single.pdf'
if with_noise and save_path is not None:
    save_path = save_path.replace('.pdf', '_noise.pdf')

# GRAPH SETTINGS:
fig_kwargs = dict(
    figsize=(32/2.54, 16/2.54),
)
super_grid_kwargs = dict(
    nrows=None,
    ncols=3,
    wspace=0,
    hspace=0,
    left=0,
    right=1,
    bottom=0,
    top=1
)
subfig_specs = dict(
    snip=(slice(None), slice(super_grid_kwargs['ncols'] - 1)),
    big=(slice(None), -1),
)
snip_grid_kwargs = dict(
    nrows=len(stages),
    ncols=None,
    wspace=0.3,
    hspace=0.1,
    left=0.1,
    right=0.93,
    bottom=0.05,
    top=0.85
)
big_grid_kwargs = dict(
    nrows=1,
    ncols=1,
    wspace=0,
    hspace=0,
    left=0.17,
    right=0.96,
    bottom=0.1,
    top=0.99
)
inset_bounds = [1.02, 0, 0.2, 1]

# PLOT SETTINGS:
colors = load_colors('../data/stage_colors.npz')
color_factors = [0.2, -0.2]
lw = dict(
    conv=1,
    bi=0.1,
    feat=3,
    big=4,
)
xlabels = dict(
    big='scale $\\alpha$',
)
xlab_big_kwargs = dict(
    y=0.01,
    fontsize=16,
    ha='center',
    va='bottom',
)
ylabels = dict(
    conv='$c_i$',
    bi='$b_i$',
    feat='$f_i$',
    big='$\\mu_f$',
)
ylab_snip_kwargs = dict(
    x=0.08,
    fontsize=20,
    rotation=0,
    ha='right',
    va='center',
)
ylab_super_kwargs = dict(
    x=0.005,
    fontsize=16,
    ha='left',
    va='center',
)
ylab_big_kwargs = dict(
    x=0,
    fontsize=20,
    ha='center',
    va='top',
)
yloc = dict(
    big=0.2,
)
letter_snip_kwargs = dict(
    x=0.01,
    y=0.9,
    ha='left',
    va='top',
    fontsize=22,
)
letter_big_kwargs = dict(
    x=0,
    yref=letter_snip_kwargs['y'],
    ha='left',
    va='top',
    fontsize=22,
)
dist_kwargs = dict(
    nbins=50,
    c='k',
    lw=1,
)
dist_fill_kwargs = dict(
    color=colors['bi'],
    lw=0.1,
)
bar_time = 0.5
bar_kwargs = dict(
    y0=0.3,
    y1=0.4,
    color='k',
    lw=0,
)
kernel = np.array([
    [1, 0.008],
    [2, 0.004],
    [3, 0.002],
])[np.array([1])]
zoom_rel = np.array([0.5, 0.525])


# EXECUTION:
for data_path in data_paths:
    print(f'Processing {data_path}')

    # Load invariance data:
    data, config = load_data(data_path, **load_kwargs)
    t_full = np.arange(data['snip_conv'].shape[0]) / config['env_rate']
    zoom_abs = zoom_rel * t_full[-1]
    zoom_inds = (t_full >= zoom_abs[0]) & (t_full <= zoom_abs[1])
    kern_ind = find_kern_specs(config['k_specs'], kerns=kernel)[0]

    # Reduce to kernel subset and crop time to zoom frame:
    data['snip_conv'] = data['snip_conv'][zoom_inds, kern_ind, ...]
    data['snip_bi'] = data['snip_bi'][zoom_inds, kern_ind, ...]
    data['snip_feat'] = data['snip_feat'][zoom_inds, kern_ind, ...]
    data['measure_feat'] = data['measure_feat'][:, kern_ind, :]
    t_full = np.arange(data['snip_conv'].shape[0]) / config['env_rate']

    # Get threshold-specific colors:
    factors = np.linspace(*color_factors, data['threshs'].size)
    colors = dict(
        conv=shade_colors(colors['conv'], factors),
        bi=shade_colors(colors['bi'], factors),
        feat=shade_colors(colors['feat'], factors),
    )

    # Adjust grid parameters:
    super_grid_kwargs['nrows'] = data['threshs'].size
    snip_grid_kwargs['ncols'] = data['example_scales'].size

    # Prepare overall graph:
    fig = plt.figure(**fig_kwargs)
    super_grid = fig.add_gridspec(**super_grid_kwargs)

    # Prepare snippet axes:
    snip_axes = {}
    for i in range(data['threshs'].size):
        subfig_specs['snip'] = (i, subfig_specs['snip'][1])
        snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
        axes = add_snip_axes(snip_subfig, snip_grid_kwargs)
        snip_axes[snip_subfig] = axes
        super_ylabel(f'{strip_zeros(100 * data["thresh_perc"][i])}%',
                     snip_subfig, axes[-1, 0], axes[0, 0], **ylab_super_kwargs)
        for ax, stage in zip(axes[:, 0], stages):
            ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
                   transform=snip_subfig.transSubfigure)
        if i == 0:
            time_bar(axes[0, 0], bar_time, **bar_kwargs)
            for ax, scale in zip(axes[0, :], data['example_scales']):
                ax.set_title(f'$\\alpha={strip_zeros(scale)}$')
    letter_subplots(snip_axes.keys(), **letter_snip_kwargs)

    # Prepare analysis axis:
    big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
    big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
    big_ax = big_subfig.add_subplot(big_grid[0, 0])
    xlabel(big_ax, xlabels['big'], **xlab_big_kwargs,
           transform=big_subfig.transSubfigure)
    ylabel(big_ax, ylabels['big'], **ylab_big_kwargs,
           transform=big_subfig.transSubfigure)
    big_ax.set_xlim(data['scales'].min(), data['scales'].max())
    big_ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
    ylimits(data['measure_feat'], big_ax, minval=0, pad=0.01)
    big_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
    letter_subplot(big_subfig, 'd', **letter_big_kwargs, ref=list(snip_axes.keys())[0])

    # Plot representation snippets per threshold:
    conv_min, conv_max = ylimits(data['snip_conv'], pad=0.02)
    for i, (subfig, axes) in enumerate(snip_axes.items()):
        dist_fill_kwargs['color'] = colors['bi'][i]

        # Plot kernel response snippets:
        plot_snippets(axes[0, :], t_full, data['snip_conv'][:, :, i],
                      thresh=data['threshs'][i], ymin=conv_min, ymax=conv_max,
                      fill_kwargs=dist_fill_kwargs, c=colors['conv'][i], lw=lw['conv'])

        # Plot binary snippets:
        plot_bi_snippets(axes[1, :], t_full, data['snip_bi'][:, :, i],
                         color=colors['bi'][i], lw=lw['bi'])
        # Plot feature snippets:
        plot_snippets(axes[2, :], t_full, data['snip_feat'][:, :, i],
                      ymin=0, ymax=1, c=colors['feat'][i], lw=lw['feat'])

        # Plot kernel response distribution:
        side_distributions(axes[0, :], data['snip_conv'][:, :, i], inset_bounds,
                           data['threshs'][i], ymin=conv_min, ymax=conv_max,
                           fill_kwargs=dist_fill_kwargs, **dist_kwargs)

    # Plot analysis results:
    handles = big_ax.plot(data['scales'], data['measure_feat'], lw=lw['big'])
    [h.set_color(c) for h, c in zip(handles, colors['feat'])]

    if save_path is not None:
        fig.savefig(save_path)
    plt.show()

print('Done.')
embed()